[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001507 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 777
[LightGBM] [Info] Number of data points in the train set: 48363, number of used features: 9
[LightGBM] [Info] Start training from score 2.915100
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001143 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 781
[LightGBM] [Info] Number of data points in the train set: 48363, number of used features: 9
[LightGBM] [Info] Start training from score 1.227560
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001125 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 746
[LightGBM] [Info] Number of data points in the train set: 48363, number of used features: 9
[LightGBM] [Info] Start training from score 92.850506
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001000 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 672
[LightGBM] [Info] Number of data points in the train set: 48363, number of used features: 9
[LightGBM] [Info] Start training from score 9.883382
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000758 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 782
[LightGBM] [Info] Number of data points in the train set: 48363, number of used features: 9
[LightGBM] [Info] Start training from score 1.321589
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001005 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 533
[LightGBM] [Info] Number of data points in the train set: 48363, number of used features: 9
[LightGBM] [Info] Start training from score 1.051508
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000726 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 741
[LightGBM] [Info] Number of data points in the train set: 48363, number of used features: 9
[LightGBM] [Info] Start training from score 5.863635
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000898 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 746
[LightGBM] [Info] Number of data points in the train set: 48363, number of used features: 9
[LightGBM] [Info] Start training from score 3.047722
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001329 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 781
[LightGBM] [Info] Number of data points in the train set: 48363, number of used features: 9
[LightGBM] [Info] Start training from score 1.613858
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001416 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 533
[LightGBM] [Info] Number of data points in the train set: 48363, number of used features: 9
[LightGBM] [Info] Start training from score 400.970473
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001039 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 777
[LightGBM] [Info] Number of data points in the train set: 48363, number of used features: 9
[LightGBM] [Info] Start training from score 2.915100
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000772 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 781
[LightGBM] [Info] Number of data points in the train set: 48363, number of used features: 9
[LightGBM] [Info] Start training from score 1.227560
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000859 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 746
[LightGBM] [Info] Number of data points in the train set: 48363, number of used features: 9
[LightGBM] [Info] Start training from score 92.850506
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001045 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 672
[LightGBM] [Info] Number of data points in the train set: 48363, number of used features: 9
[LightGBM] [Info] Start training from score 9.883382
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001002 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 782
[LightGBM] [Info] Number of data points in the train set: 48363, number of used features: 9
[LightGBM] [Info] Start training from score 1.321589
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000862 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 533
[LightGBM] [Info] Number of data points in the train set: 48363, number of used features: 9
[LightGBM] [Info] Start training from score 1.051508
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000679 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 741
[LightGBM] [Info] Number of data points in the train set: 48363, number of used features: 9
[LightGBM] [Info] Start training from score 5.863635
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000850 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 746
[LightGBM] [Info] Number of data points in the train set: 48363, number of used features: 9
[LightGBM] [Info] Start training from score 3.047722
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001397 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 781
[LightGBM] [Info] Number of data points in the train set: 48363, number of used features: 9
[LightGBM] [Info] Start training from score 1.613858
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000882 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 533
[LightGBM] [Info] Number of data points in the train set: 48363, number of used features: 9
[LightGBM] [Info] Start training from score 400.970473
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.002339 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 777
[LightGBM] [Info] Number of data points in the train set: 48363, number of used features: 9
[LightGBM] [Info] Start training from score 2.915100
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001129 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 781
[LightGBM] [Info] Number of data points in the train set: 48363, number of used features: 9
[LightGBM] [Info] Start training from score 1.227560
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001260 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 746
[LightGBM] [Info] Number of data points in the train set: 48363, number of used features: 9
[LightGBM] [Info] Start training from score 92.850506
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000990 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 672
[LightGBM] [Info] Number of data points in the train set: 48363, number of used features: 9
[LightGBM] [Info] Start training from score 9.883382
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001031 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 782
[LightGBM] [Info] Number of data points in the train set: 48363, number of used features: 9
[LightGBM] [Info] Start training from score 1.321589
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001062 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 533
[LightGBM] [Info] Number of data points in the train set: 48363, number of used features: 9
[LightGBM] [Info] Start training from score 1.051508
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000810 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 741
[LightGBM] [Info] Number of data points in the train set: 48363, number of used features: 9
[LightGBM] [Info] Start training from score 5.863635
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001181 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 746
[LightGBM] [Info] Number of data points in the train set: 48363, number of used features: 9
[LightGBM] [Info] Start training from score 3.047722
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000915 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 781
[LightGBM] [Info] Number of data points in the train set: 48363, number of used features: 9
[LightGBM] [Info] Start training from score 1.613858
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000897 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 533
[LightGBM] [Info] Number of data points in the train set: 48363, number of used features: 9
[LightGBM] [Info] Start training from score 400.970473
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001036 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 777
[LightGBM] [Info] Number of data points in the train set: 48363, number of used features: 9
[LightGBM] [Info] Start training from score 2.915100
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000776 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 781
[LightGBM] [Info] Number of data points in the train set: 48363, number of used features: 9
[LightGBM] [Info] Start training from score 1.227560
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001195 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 746
[LightGBM] [Info] Number of data points in the train set: 48363, number of used features: 9
[LightGBM] [Info] Start training from score 92.850506
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000909 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 672
[LightGBM] [Info] Number of data points in the train set: 48363, number of used features: 9
[LightGBM] [Info] Start training from score 9.883382
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000980 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 782
[LightGBM] [Info] Number of data points in the train set: 48363, number of used features: 9
[LightGBM] [Info] Start training from score 1.321589
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000838 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 533
[LightGBM] [Info] Number of data points in the train set: 48363, number of used features: 9
[LightGBM] [Info] Start training from score 1.051508
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001201 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 741
[LightGBM] [Info] Number of data points in the train set: 48363, number of used features: 9
[LightGBM] [Info] Start training from score 5.863635
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000929 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 746
[LightGBM] [Info] Number of data points in the train set: 48363, number of used features: 9
[LightGBM] [Info] Start training from score 3.047722
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000838 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 781
[LightGBM] [Info] Number of data points in the train set: 48363, number of used features: 9
[LightGBM] [Info] Start training from score 1.613858
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001138 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 533
[LightGBM] [Info] Number of data points in the train set: 48363, number of used features: 9
[LightGBM] [Info] Start training from score 400.970473
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001301 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 777
[LightGBM] [Info] Number of data points in the train set: 48363, number of used features: 9
[LightGBM] [Info] Start training from score 2.915100
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000754 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 781
[LightGBM] [Info] Number of data points in the train set: 48363, number of used features: 9
[LightGBM] [Info] Start training from score 1.227560
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001045 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 746
[LightGBM] [Info] Number of data points in the train set: 48363, number of used features: 9
[LightGBM] [Info] Start training from score 92.850506
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000841 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 672
[LightGBM] [Info] Number of data points in the train set: 48363, number of used features: 9
[LightGBM] [Info] Start training from score 9.883382
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001249 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 782
[LightGBM] [Info] Number of data points in the train set: 48363, number of used features: 9
[LightGBM] [Info] Start training from score 1.321589
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000844 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 533
[LightGBM] [Info] Number of data points in the train set: 48363, number of used features: 9
[LightGBM] [Info] Start training from score 1.051508
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000771 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 741
[LightGBM] [Info] Number of data points in the train set: 48363, number of used features: 9
[LightGBM] [Info] Start training from score 5.863635
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000782 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 746
[LightGBM] [Info] Number of data points in the train set: 48363, number of used features: 9
[LightGBM] [Info] Start training from score 3.047722
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001598 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 781
[LightGBM] [Info] Number of data points in the train set: 48363, number of used features: 9
[LightGBM] [Info] Start training from score 1.613858
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000851 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 533
[LightGBM] [Info] Number of data points in the train set: 48363, number of used features: 9
[LightGBM] [Info] Start training from score 400.970473
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000818 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 777
[LightGBM] [Info] Number of data points in the train set: 48363, number of used features: 9
[LightGBM] [Info] Start training from score 2.915100
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000804 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 781
[LightGBM] [Info] Number of data points in the train set: 48363, number of used features: 9
[LightGBM] [Info] Start training from score 1.227560
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000944 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 746
[LightGBM] [Info] Number of data points in the train set: 48363, number of used features: 9
[LightGBM] [Info] Start training from score 92.850506
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000818 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 672
[LightGBM] [Info] Number of data points in the train set: 48363, number of used features: 9
[LightGBM] [Info] Start training from score 9.883382
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001805 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 782
[LightGBM] [Info] Number of data points in the train set: 48363, number of used features: 9
[LightGBM] [Info] Start training from score 1.321589
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000825 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 533
[LightGBM] [Info] Number of data points in the train set: 48363, number of used features: 9
[LightGBM] [Info] Start training from score 1.051508
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000740 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 741
[LightGBM] [Info] Number of data points in the train set: 48363, number of used features: 9
[LightGBM] [Info] Start training from score 5.863635
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000801 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 746
[LightGBM] [Info] Number of data points in the train set: 48363, number of used features: 9
[LightGBM] [Info] Start training from score 3.047722
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001396 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 781
[LightGBM] [Info] Number of data points in the train set: 48363, number of used features: 9
[LightGBM] [Info] Start training from score 1.613858
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000867 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 533
[LightGBM] [Info] Number of data points in the train set: 48363, number of used features: 9
[LightGBM] [Info] Start training from score 400.970473